Clusterin/Apolipoprotein <scp>J</scp> immunoreactivity is associated with white matter damage in cerebral small vessel diseases
Bibliographic record
Abstract
AIM: Brain clusterin is known to be associated with the amyloid-β deposits in Alzheimer's disease (AD). We assessed the distribution of clusterin immunoreactivity in cerebrovascular disorders, particularly focusing on white matter changes in small vessel diseases. METHODS: Post-mortem brain tissues from the frontal or temporal lobes of a total of 70 subjects with various disorders including cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), cerebral amyloid angiopathy (CAA) and AD were examined using immunohistochemistry and immunofluorescence. We further used immunogold electron microscopy to study clusterin immunoreactivity in extracellular deposits in CADASIL. RESULTS: Immunostaining with clusterin antibodies revealed strong localization in arterioles and capillaries, besides cortical neurones. We found that clusterin immunostaining was significantly increased in the frontal white matter of CADASIL and pontine autosomal dominant microangiopathy and leukoencephalopathy subjects. In addition, clusterin immunostaining correlated with white matter pathology severity scores. Immunostaining in axons ranged from fine punctate deposits in single axons to larger confluent areas with numerous swollen axon bulbs, similar to that observed with known axon damage markers such as non-phosphorylated neurofilament H and the amyloid precursor protein. Immunofluorescence and immunogold electron microscopy experiments showed that whereas clusterin immunoreactivity was closely associated with vascular amyloid-β in CAA, it was lacking within the granular osmiophilic material immunolabelled by NOTCH3 extracelluar domain aggregates found in CADASIL. CONCLUSIONS: Our results suggest a wider role for clusterin associated with white matter damage in addition to its ability to chaperone proteins for clearance via the perivascular drainage pathways in several disease states.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".